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Found 1,258 Skills
Fix all ESLint and TypeScript errors with parallel processing using snipper agents
This skill analyzes code for design quality improvements across 8 dimensions: Naming, Object Calisthenics, Coupling & Cohesion, Immutability, Domain Integrity, Type System, Simplicity, and Performance. Ensures rigorous, evidence-based analysis by: (1) Understanding code flow first via implementation-analysis protocol, (2) Systematically evaluating each dimension with specific criteria, (3) Providing actionable findings with file:line references. Triggers when users request: code analysis, design review, refactoring opportunities, code quality assessment, architecture evaluation.
This skill provides a systematic approach for investigating and fixing bugs and issues in the codebase. Use this skill when users report bugs, unexpected behavior, test failures, or request fixes for specific issues. The skill guides through issue registration, root cause analysis, impact assessment, minimal code changes, quality validation, and E2E testing.
Use when user asks to "deep review the code", "thorough code review", "multi-pass review", or when orchestrating the Phase 9 review loop. Provides review pass definitions (code quality, security, performance, test coverage), signal detection patterns, and iteration algorithms.
Conduct rigorous, adversarial code reviews with zero tolerance for mediocrity. Use when users ask to "critically review" my code or a PR, "critique my code", "find issues in my code", or "what's wrong with this code". Identifies security holes, lazy patterns, edge case failures, and bad practices across Python, R, JavaScript/TypeScript, SQL, and front-end code. Scrutinizes error handling, type safety, performance, accessibility, and code quality. Provides structured feedback with severity tiers (Blocking, Required, Suggestions) and specific, actionable recommendations.
Triage unresolved PR review comments, produce a severity-ordered fix plan, then resolve or fix each issue with subagents. Use when addressing PR feedback before merge.
Conduct thorough code reviews with structured feedback on security, performance, architecture, and testing. Generates trackable review documents with prioritized issues (critical/required/suggestions) and educational content. Use when reviewing PRs or code changes. Triggers on "review this code", "code review", "review PR".
Captures quality metrics baseline (tests, coverage, type errors, linting, dead code) by running quality gates and storing results in memory for regression detection. Use at feature start, before refactor work, or after major changes to establish baseline. Triggers on "capture baseline", "establish baseline", or PROACTIVELY at start of any feature/refactor work. Works with pytest output, pyright errors, ruff warnings, vulture results, and memory MCP server for baseline storage.
Modo Elite Coder + UX Pixel-Perfect otimizado especificamente para Gemini 3.1 Pro High. Workflow completo com foco em qualidade máxima e eficiência de tokens.
Use when marking a task as complete, finishing a feature, or claiming a bug is fixed. Ensures functional resolution is verified with evidence before closing.
Scans code against 17 named design smells and produces a structured diagnostic report. Use when reviewing a PR for design quality, evaluating unfamiliar code against a comprehensive checklist or when the user asks for a red flags scan. Not for diagnosing why code feels complex (use complexity-recognition) or evaluating whether a PR maintains design trajectory (use code-evolution).
Run after making Docyrus API changes to catch bugs, performance issues, and code quality problems. Use when implementing or modifying code that uses Docyrus collection hooks (.list, .get, .create, .update, .delete), direct RestApiClient calls, query payloads with filters/calculations/formulas/childQueries/pivots, or TanStack Query integration with Docyrus data sources. Triggers on tasks involving Docyrus API logic, data fetching, mutations, or query payload construction.